Sistem Pakar Diagnosa Penyakit Kulit Kucing Menggunakan Metode Naive Bayes Berbasis Web
DOI:
https://doi.org/10.35870/jtik.v6i3.466Keywords:
NaiveBayes, Cat Skin Disease, PHP, Expert SystemAbstract
This study aims to implement the Naive Bayes method in a skin disease diagnosis system in cats. The Naive Bayes method in this application system uses disease probability calculations, calculates the probability of symptoms, calculates the posterior probability of disease, and calculates the maximum value of each disease. This method is used to analyze the results of the diagnosis of skin diseases in cats based on the symptoms of skin diseases. Data analysis on this application system is based on the results of tests carried out 50 times to produce disease diagnosis results from the user and calculated by the system to produce the same diagnostic results as calculations performed manually. With this facility, cat owners can provide first aid to help their cat before being taken to the clinic and further examined by a veterinarian.Downloads
References
Samhan, L.F., Alfarra, A.H. and Abu-Naser, S.S., 2021. An Expert System for Knee Problems Diagnosis. International Journal of Academic Information Systems Research (IJAISR), 5(4).
Burhani, H.R., Fitri, I. and Andrianingsih, A., 2021. Perbandingan Naïve bayes dan Certainty factor pada Sistem Pakar Untuk Mendiagnosa Dini Penyakit Glaukoma. Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi), 5(3), pp.291-299.
Yunas, R.A.D., Triayudi, A. and Sholihati, I.D., 2021. Implementasi Sistem Pakar untuk Mendeteksi Virus Covid-19 dengan Perbandingan Metode Naïve Bayes dan Certainty Factor. Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi), 5(3), pp.338-345.
Harefa, K., 2020. Sistem Pendukung Keputusan Kelayakan Pemberian Pinjaman dengan Metode Analytical Hierarchy Process (AHP) dan Simple Additive Weighting (SAW). Jurnal Informatika Universitas Pamulang, 5(2), pp.136-145.
Gaihre, A., Wu, Z., Yao, F. and Liu, H., 2019, June. Xbfs: exploring runtime optimizations for breadth-first search on gpus. In Proceedings of the 28th International Symposium on High-Performance Parallel and Distributed Computing (pp. 121-131).
Spampinato, D.G., Sridhar, U. and Low, T.M., 2019, June. Linear algebraic depth-first search. In Proceedings of the 6th ACM SIGPLAN International Workshop on Libraries, Languages and Compilers for Array Programming (pp. 93-104).
Kartika, D. and Junaidi, A., 2018. Aplikasi Diagnosa Penyakit Lambung Dengan Metode Forward Chaining. Jurnal Teknologi Informatika dan Komputer, 4(2), pp.71-77.
Rizal, S. and Wali, M., 2020. The Impact of Using Technology (Technostress) with the Forward Chaining Method as a Decision Support System: The Impact of Using Technology (Technostress) with the Forward Chaining Method as a Decision Support System. Jurnal Mantik, 4(1), pp.521-527.
Adrianto, L.B., Wahyuddin, M.I. and Winarsih, W., 2021. Implementasi Deep Learning untuk Sistem Keamanan Data Pribadi Menggunakan Pengenalan Wajah dengan Metode Eigenface Berbasis Android. Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi), 5(1), pp.89-96.
Fadhilah, A.M., Wahyuddin, M.I. and Hidayatullah, D., 2021. Analisis Faktor yang Mempengaruhi Perokok Beralih ke Produk Alternatif Tembakau (VAPE) menggunakan Metode K-Means Clustering. Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi), 5(2), pp.219-225.
Ferdiansyah, Rizki, W. Muflikhah, Lailil. Adinugroho, Sigit. “Sistem Pakar Diagnosis Penyakit Pada Kambing Menggunakan Metode Naive Bayes dan Certainty Factorâ€. Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, Vol. 2, No. 2, hlm. 451-458, Februari 2018.
Satya, Tungga, D. Hidayat, Nurul. Sutrisno. “Sistem Pakar Diagnosis Penyakit Sapi Ternak Potong Menggunakan Metode Naïve Bayes – Certainty Factorâ€. Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, Vol. 2, No. 10, hlm. 3406-3410, Oktober 2018.
Ambica, A., dkk. 2013. An Efficient Expert System for Diabetes by Naïve Bayesian Classifier. Dadi Institute of Engineering and Technology (Affiliated to JNTUK). Andhra Pradesh.
KUSRINI. 2008. Aplikasi Sistem Pakar Menentukan Faktor Kepastian Pengguna dengan Metode Kuantifikasi Pertanyaan. Yogyakarta: Andi.
Kusrini. (2008). Sistem Pakar Teori dan Aplikasi. Yogyakarta: Penerbit Andi Offset. ISBN: 978-979-763-172-7.
Insani, Muhammad Ilham, Alamsyah, Anggyi Trimawan Putra. “Implementation of Expert System for Diabetes Diseases using Naïve Bayes and Certainty Factor Methodsâ€.Vol. 5, No. 2, Nov 2018.
Sunardi dan Desi Saputra. 2011. Sistem Pakar Untuk Mendiagnosa Penyakit Pada Kucing Melalui Perangkat Mobile. Tugas Akhir. Palembang : STMIK GI MDP.
Nugroho, Fride. 2012. Sistem Pakar Untuk Mendiagnosa Penyakit Pada Kucing. Tugas Akhir. Semarang : UDINUS.
Informatikalogi.com (2017). Algoritma Naive Bayes.
Wijaya, B. A., & Tanjung, J. P. (2020). An Expert System For Diagnosis Eye Diseases On Human Using Certainty Factor Method Based Web.
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